#pragma once

#include "./core/omp_config.h"

#include "./utils/vector.h"
#include "./utils/matrix.h"

#include "./numerics/matmul.h"

#include <math.h>




namespace neural_networks{

	template <typename T>
	struct Optimizer_Adagrad{

			T learning_rate = T{1};
			T current_learning_rate = learning_rate;
			T decay = T{0};
			T epsilon = T{1e-7};
			uint64_t iterations = 0;
		
			// Default Constructor
			Optimizer_Adagrad() = default;

			// Constructor
	    	explicit Optimizer_Adagrad(const T lr, const T lr_decay, const T epsilons): learning_rate(lr), current_learning_rate{lr}, decay(lr_decay), epsilon(epsilons) {}

	    	void pre_update_params(){
	    		if(decay){
	    			current_learning_rate = learning_rate * (T{1}/(T{1}+(decay*iterations)));
	    			//std::cout << current_learning_rate << std::endl;
	    		}
	    	}

	    	template <typename Layer>
	        void update_params(Layer& layer){



        		// if layer does not contain cache arrays, create them filled with zeros.
        		if ((layer.weight_cache.rows() != layer.weights.rows()) || (layer.weight_cache.cols() != layer.weights.cols())){
        			layer.weight_cache.resize(layer.weights.rows(), layer.weights.cols(), T{0});
        		}
        		if (layer.bias_cache.size() != layer.biases.size()){
        			layer.bias_cache.resize(layer.biases.size(), T{0});
        		}

        		// Update cache with squared current gradients
        		for (uint64_t i = 0; i < layer.weights.rows(); ++i){
        			for (uint64_t j = 0; j < layer.weights.cols(); ++j){
        				layer.weight_cache(i,j) = layer.dweights(i,j)*layer.dweights(i,j);
        			}
        		}
        		
        		for (uint64_t i = 0; i < layer.biases.size(); ++i){ // can maybe be included when updating weights (saves time)
        			layer.bias_cache[i] = layer.dbiases[i]*layer.dbiases[i];
        		}

        		// Vanilla SGD parameter update + normalization with squared rooted cache
	        	for (uint64_t i = 0; i < layer.weights.rows(); ++i){
	        		for (uint64_t j = 0; j < layer.weights.cols(); ++j){
	        			layer.weights(i,j) -= (current_learning_rate*layer.dweights(i,j)) / (std::sqrt(layer.weight_cache(i,j)) + epsilon);
	        		}
	        	}
	        	for (uint64_t i = 0; i < layer.biases.size(); ++i){
	        		layer.biases[i] -= (current_learning_rate*layer.dbiases[i]) / (std::sqrt(layer.bias_cache[i]) + epsilon);
	        	}
	        }

	    	void post_update_params(){
	    		iterations++;
	    	}

	};

} // end namespace neural_networks